Simulation of Grinding by Means of the Finite Element Method and Artificial Neural Networks

Background

Grinding is a manufacturing process characterized by complex relationships between process parameters, workpiece and cutting tools characteristics as well as quality features of the finished products. Researchers have utilized modeling and simulation for several decades, now, in order to investigate grinding more thoroughly, explain phenomena taking place during the process and achieve its optimization.

Grinding models are used for the prediction of surface roughness, wear characteristics, grinding forces, grinding energy and surface integrity among others. Grinding forces are essential for calculating grinding energy, which in turn determines surface integrity; grinding energy is transformed into heat dissipated into wheel, chip, workpiece and cutting fluid, if present. Excessive heat loading of the workpiece leads to the formation of heat affected zones. This heat input is responsible for a number of defects in the workpiece like metallurgical alterations, microcracks and residual stresses. High surface temperatures are connected to these phenomena and may lead to grinding burn (Malkin, 1978; Malkin and Guo, 2007). Because of the importance of the heat transfer problems in grinding, thermal modelling has proved to be of utmost importance. Thermal models relate all the process parameters in order to determine grinding temperatures.